Graphs can be used to great effect in publication. They can summarize trends, patterns and relationships between variables. They can illustrate and amplify the main messages of the publication and inspire the reader to continue reading. Graphs are generally better understood and interpreted by the average reader, and therefore appeal to a wider audience. If done well, they can give readers a quick and easy understanding of the differences and similarities between women and men.

Every graph should make a point, which can be given in the title. Nevertheless, in many publications, titles state the subject and the coverage of data in the graph. In this case, the title should start with the key word(s) of the statistics presented.

There are many types of charts. The type of chart used depends on the kind of data used in the analysis and the point the authors wish to make. Choosing the correct chart can make the difference between providing the reader with a strong message and confusing the reader.

Line charts provide a clear picture of changes over time or over age cohorts that cannot easily be discovered in data tables. Time-series data that are often presented in line charts include life expectancies at birth, infant mortality, literacy rates and labour force participation rates. In general, it is expected that advances in human development over time will be reflected in declining infant mortality rates and increasing literacy rates and life expectancies, while labour force participation rates are expected to respond to changes in overall market and trade conditions. Chart IV.1, for example, shows trends in life expectancy at birth for women and men in South Africa.

It is generally recommended that charts start from zero at the y-axis of a quantitative variable, so that the differences or similarities between women and men are not distorted. At the same time, however, it is important for the comparison between women and men to be facilitated. In this case all the values of life expectancy are concentrated above the age of 35. The focus on differences between women and men in the chart allows the viewer to see how the HIV/AIDS epidemic in South Africa changed in the 1990s with regard to women’s and men’s trends in life expectancies.

Chart IV. 1

Life expectancy at birth by sex, South Africa, 1950-2010

Source: United Nations, 2011.

Line charts are also useful in revealing changes from one age cohort to another in labour force participation, employment or literacy, for example. Chart IV.2, for instance, shows age patterns in labour force participation for women and men in Chile for two points in time. The chart illustrates three main points: (a) at all ages, labour force participation rates are lower for women than for men; (b) in the last two decades women’s participation rates increased more than men’s participation rates; and (c) in the most recent year observed, women tended to withdraw from the labour market after the age of 30.

Chart IV. 2

Labour force participation rate by age group, by sex, Chile, 1990 and 2008.

Bar charts are common in the presentation of gender statistics. One of the axes, usually the x-axis, is formed by a qualitative variable with distinct categories. This variable can be sex or another breakdown variable such as urban/rural area, region, or wealth quintile. The other axis can represent absolute frequencies or percentages, sums or averages. Bar charts can be used to illustrate data that do not vary too greatly in magnitude.

Chart IV.3 is an example of a simple vertical bar chart. It shows the percentage of women in India who have ever experienced physical violence for different categories of wealth, ordered from the poorest quintile (poorest 20 per cent of the population) to the wealthiest quintile (wealthiest 20 per cent of the population). Other examples of simple bar charts may include total fertility rate by region, antenatal care by urban/rural area, or proportion of women married before the age of 18 by level of education.

Chart IV. 3 Women age 15-49 who have experienced physical violence since age 15 by wealth quintiles, India, 2005-2006

Source: International Institute for Population Sciences and Macro International, 2007.

Grouped (or clustered) bar charts present the same characteristic for two or more categories of population at the same time, thereby facilitating comparisons. Often, the values of a characteristic for women and men are shown as two sets of differently colored or shaded bars side by side for each category. For example, in chart IV.4, data on school attendance in Yemen is presented for girls and boys side by side within two categories of population (the poorest and the wealthiest quintiles). It is shown that girls have lower school participation rates than boys in both wealth groups; however, the gender gap is much more substantial in the poorest group of population.

Chart IV. 4

Primary school net attendance rate for children in the poorest and wealthiest quintiles, Yemen, 2006

Source: Yemen, Ministry of Health and Population and others, 2008.

If more categories or data points need to be illustrated, the bars can become too thin and difficult to interpret. In such cases it is recommended that some dot charts be used instead of grouped bar charts. For example, in comparison to chart IV.4, chart IV.5 presents gender differences in school attendance for all wealth quintiles and for urban and rural areas. Chart IV.5 shows the disadvantage of girls in school participation in all groups and how this disadvantage is greater in the poorer population and in rural areas.

Chart IV. 5

Primary school net attendance rates for girls and boys by wealth quintiles and by urban/rural areas, Yemen, 2006

Similar to the grouped bar charts, stacked bar charts illustrate data sets consisting of two or more categories. Stacked bar charts can be used for most kinds of data, but they are most effective for categories that add up to 100 per cent. A common problem with stacked bar charts is that one or more segments are too short to be visible on the scale. Another problem is that using more than three segments of the bar can make it difficult to compare one bar to another.

Some stacked charts illustrate the percentage distribution by sex within various categories of variables, such as the share of women and men among categories of occupations. Chart IV.6 is one example of this type of stacked chart, and it shows that, in Viet Nam, women hold only a small proportion of property titles.

Chart IV. 6

Distribution of property titles by sex of the owner and urban/rural areas, Viet Nam, 2006

Other stacked charts, however, can illustrate the distribution of variables within the female and male population, such as the distribution of female and male deaths by cause of death or the distribution of female and male employment by sector of employment. Chart IV.7, for example, shows that women’s employment in Morocco is concentrated primarily in agriculture, while men’s employment is concentrated primarily in services and secondarily in agriculture.

Bar charts can also be presented horizontally. They are often used when many categories need to be presented, or when the categories presented have long labels. Men and women can be presented side by side for each category, as in chart IV.8. Similar to vertical bar charts, when a graph needs to display the sex distribution within a category and the values for women and men add up to 100 per cent, a stacked bar chart should be considered.

Chart IV. 8

Proportion of obese persons, by sex and wealth quintile, Egypt, 2008

Source: El-Zanaty and Way, 2009.

Horizontal bar charts are also ideal for showing time-use data, because the left-to-right motion (in Western cultures) on the x-axis generally implies the passage of time. Chart IV.9 provides such an example.

Chart IV. 9

Average time spent on care for children, the sick and the elderly by sex, urban/rural areas and marital status, Pakistan, 2007 (minutes per day in total population aged 10 and higher)

Bar charts are often used to present gender statistics for different regions of a country. When there are many regions to be presented, a horizontal bar chart may be preferred. It is important that the regions considered are presented in such a way that they facilitate comparisons between women and men within and between the regions. Presenting the regions alphabetically is seldom a good solution. When no other dimension is the focus of analysis (such as the level of economic or human development of the region, for example), it is important that the regions are presented in the graph according to the rank of values observed for women or, less frequently, for men. Ranking of the regions by gender gap may also be considered if it would not make the graph too confusing.

Another way to use a horizontal bar chart is to plot against each other (extending left and right from the y-axis) two variables that are visibly correlated. An example of a pair of such variables would be the proportion of women married before the age of 18 and the adolescent fertility rate, both disaggregated by region; or the total fertility rate and women’s contraceptive use, both disaggregated by region. The two variables considered for this type of plot do not need to have the same scale.

A variation on the use of horizontal bars is the “age and sex pyramid”. Traditionally, age and sex pyramids plot the age composition of the population of women and men as horizontal bars originating from the y-axis, using the absolute number of women and men by age group. Because they use absolute numbers, age and sex pyramids tend to emphasize the concentration of population in particular age groups. Alternatively, this type of chart can be constructed using percentages instead of absolute numbers, emphasizing the groups where women or men are overrepresented. For example, chart IV.10 illustrates the composition of population in Swaziland by sex, age group and level of educational attainment. In comparison, chart IV.11 illustrates, for the same country, the proportion of women and men with at least secondary education within age group.

Chart IV. 10

Distribution of population by sex, age group and educational attainment, Swaziland, 2007

Source: United Nations, 2012.

Chart IV. 11

Proportion of population with at least secondary education, by sex and age group, Swaziland, 2007

Source: United Nations, 2012.

Other examples of age and sex pyramids include foreign-born population by sex, age group and marital status, or proportion of population smoking by sex and age group.

Pie charts are suitable for illustrating the percentage distribution of qualitative variables and are an alternative to bar charts. Pie charts must always show shares that total 100 per cent. A common error with pie charts is to show too many categories, resulting in labels that are hard to read or shares that are too narrow. When too many categories need to be compared, bar charts are more suitable.

Pie charts are best used when only one or two shares of the whole are shown for different years, different population groups or different related categories. For example, chart IV.12 shows the percentage of women married before the age of 18 in urban areas compared to rural areas. Other examples include the share of time used by women in total time invested by women and men in various types of unpaid housework, or the share of women among managers at two points in time.

Chart IV. 12

Proportion of women married before the age of 18 in urban and rural areas, the Gambia, 2005-2006

Scatter plots are often used to show the relationship between two variables. The two variables are plotted against each other in order to show the patterns of their grouping. Scatter plots are also used to identify and analyse outliers in the data.

Scatter plots are particularly useful when many data points need to be displayed, such as in the case of a large number of regions or subregions of a country that cannot be easily presented in tables or bar charts. Chart IV.13, for example, shows school attendance rates for girls plotted against school attendance for boys in the states of India. The dots that are close to the diagonal represent the states where girls and boys have similar school attendance rates. This is the case for most of the states in India; however, there are a few exceptions. A number of states with generally lower school attendance have higher rates for boys than for girls. These particular cases may be highlighted on the graph.

Chart IV. 13

School attendance rates for children aged 6-17 by sex and state, India, 2005-2006

Source: International Institute for Population Sciences and Macro International, 2007.